Keywords: Control applications, Industrial control, Power systems
Abstract: -- A model reference adaptive system (MRAS) based speed estimator for sensorless induction motor (IM) drive is proposed in this paper. The MRAS is formed with flux rotor and the estimated stator current vector. The reference model utilizes measured current vector. On the other hand, the adjustable model uses the estimated stator current vector. The current is estimated through the solution of machine state equations. The performance of the estimator under regeneration is an important aspect, which is studied in this paper through the small signal analysis. In this paper, the principle and method of speed estimation and control set-up are described, as well as the results of an experiment that verified the effectiveness of the proposed method.

Keywords: Modeling and simulation, Control algorithms implementation
Abstract: In this paper, we will discuss the technique of ripple correlation control (RCC)applied to photovoltaic maximum power point tracking (MPPT). To deliver maximum power from the photovoltaic system to the load under climatic variations, and to control a boost converter,MPPTbased on ripple correlation control (RCC) technique has been used. Also to improve the performance of this MPPT and to overcome the uncertainties and perturbations in the photovoltaic system, a strategy of two-layer control structure are developed to effectively handle it. The first part of the strategy use the ripple correlation control (RCC) technic, and the second part use aPI controller. This algorithm was experimentally implemented. Data acquisition and control system were implemented using dSPACE 1104. The practical results show that the proposed system tracked the maximum power accurately and successfully under the change of solar irradiation.

Keywords: Renewable Energy, Modeling and simulation, Industrial control
Abstract: In this paper, the performance of the wind energy conversion system had been improved by controlling all the components of the system, we have used MPPT control methods to extract the maximum of power, and we have developed the direct vector control to control the double fed induction generator based on the vector control of stator flux orientation using the Matlab/Simulink software on the other hand. The obtained simulation results show that the used control techniques are well suited to well operate the wind energy conversion system.

Keywords: Optimization, Renewable Energy, Modeling and simulation
Abstract: The power produced from PV panel and wind turbine generators are based on weather condition, as a result this system is unreliable. The hybridization of this system with another source such as batteries or diesel generator increases significantly the reliability of the whole system. This paper recommends an optimization method of hybrid solar-wind battery systems based on BSA (Backtracking Search Algorithm) algorithm, following its steps the BSA algorithm attains the global optimum with reasonable time and presents a good results comparing with other algorithm (Genetic Algorithm). The decision variables have included in the optimization process are the number of PV module, the number of wind turbines and the number of batteries.

Keywords: Renewable Energy, Networks optimization
Abstract: This paper deals with an optimal sizing approach to optimize the configurations of a hybrid stand-alone wind/PV system with storage. The methodology aims at finding the size of the system for a set of components, which achieved the requirements of the desired reliability with the lowest value of the average cost of energy. The load and the loss of power supply probability chosen under the criterion of a minimum system cost, a number of optimal batteries and photovoltaic modules are calculated. Thus, from hourly data of solar radiation, ambient temperature and wind speed, we calculated the energy produced by the wind turbine and photovoltaic generator analyzing period of one year. These data come from measurements at the airport of Ain Arnat in Setif town. A profile type of Consumption has been adopted. He fits the profile generally found in remote sites.

Keywords: Fault detection and Diagnostics, Industrial control, Robust control and Hinfty control
Abstract: This paper investigates the problem of fault detection for hybrid dynamical system. In the present work, the obtained LMI in cite{Benzaouia 2015} is used to design an observer with its corresponding residual, making possible fault detection of the winding machine. Also, it is possible to find by the same LMI a controller-based observer to stabilize the closed-loop system.

Keywords: Fault detection and Diagnostics, Linear and nonlinear systems, Estimations and identification
Abstract: In this paper, the problem of sensor fault diagnosis of continuous-time descriptor linear systems is investigated by using a descriptor observer. An augmented descriptor observer is constructed to simultaneously estimate state and sensor faults. The sensor fault is considered as an auxiliary state variable. The admissibility of the descriptor observer is investigated in term of Linear Matrix Inequalities (LMIs), so they are tractable using numerical computations. Finally, a numerical example and an application to a suspension model illustrate the ability of the proposed fault diagnosis approach.

Keywords: Fault detection and Diagnostics, Linear and nonlinear systems, Modeling and simulation
Abstract: To improve reliability, safety and effectiveness of supervision advanced methods, fault diagnosis become increasingly important for many technical processes. Here in our work, we provide a framework to investigate the problem of actuator fault and curtail it by proposing an effective new formulation using subsystems modeling and the intervals observer based. The paper focuses on developing a fault diagnosis scheme and it includes conditions for detection probability. Using the diesel engine model, simulations and experimental results evaluate the advantages of such interval observer approach for a simple actuator fault detection and isolation.

Keywords: Fault detection and Diagnostics, Signal processing, Modeling and simulation
Abstract: This paper presents the use of the energy obtained from the stator current analysis using discrete wavelet transform (DWT) in order to diagnose the inter turn short circuit fault in induction motor operating at different loads. The effects of the load for the stator inter turn short circuit fault detection and diagnosis is studied and discussed through simulations.

Keywords: Fault detection and Diagnostics, Signal processing, Modeling and simulation
Abstract: In this paper, a method for inter-turn short circuit In this paper, a method for inter-turn short circuit fault detection and diagnosis in the induction motor is presented. The technique is based on the analysis of the stator current and electromagnetic torque, using the discrete wavelet transform (DWT) at the start-up electromagnetic torque (non-stationary state). The method is used in order to remedy the problem from using the classical signal-processing (FFT). The performance presented by using of the DWT: its ability to provide a local representation of the non stationary current signals for the healthy and faulty state.

Keywords: Fuzzy and neural systems, Multivariable control, Linear and nonlinear systems
Abstract: This paper investigates the stability problem of large scale nonlinear systems described by the interconnected T-S fuzzy-models. Using an integral Lyapunov functional and adding slack variables, less conservative stability conditions derived in LMI terms are proposed. Simulation examples are presented to illustrate and to compare the effectiveness of the proposed stability techniques.

Keywords: Intelligent and AI based control
Abstract: This paper presents a data fusion system, which models and handles uncertainties in buildings, in order to design a smart heating system. The data fusion model provides a temperature trend that will be inserted in smart and predictive heating systems in order to reduce energy consumption in the building. The multilevel data fusion model is based on the belief functions theory. Different simulations were carried out and give very satisfactory results, in spite of the complexity of the data.

Keywords: Robust control and Hinfty control, Motion control
Abstract: A new adaptive fuzzy approach is combined with synergetic control technique is presented in this paper. Approximation of system dynamics is handled by fuzzy inference systems while synergetic control is applied in a classic tracking problem. A fast terminal synergetic approach is used to further enhance robustness via finite time convergence. Lyapunov stability analysis guarantees that the ap-proximation is stable and convergence speed is improved. Simulation of a simple application is used to demonstrate the efficiency of the proposed algorithm. It is found through sim-ulation results that the latter provides better performance than conventional synergetic method.

Keywords: Bond graphs, Fault detection and Diagnostics
Abstract: This paper deals with robust Fault Detection and Isolation (FDI) to measurement uncertainties using Hybrid Bond Graph (HBG) approach to improve the robustness of detection in presence of measurement uncertainties. The scientific interest of this work is use of one tool (Bond Graph) not only for modelling of hybrid system and measurement uncertainties but also for generation of robust fault indicators and thresholds. For this task all the measurement uncertainties are modelled on the Hybrid Bond Graph (HBG) model in derivative preferred causality. Based on the structural and causal proprieties of the bond graph tool, the generated Analytical Redundancy Relations (ARRs) are robust with respect to measurement uncertainties presented by thresholds. Furthermore, those fault indicators called Generalized Analytical Redundancy Relations (GARRs) are valid at all modes and derived systematically from an HBG model with a specific causality assignment. This causality assignment is guided by the preferred causality of controlled junctions. An HBG with such causality assignment is named Diagnostic Hybrid Bond Graph (DHBG). A systematic causality assignment procedure, named Sequential Causality Assignment Procedure for Hybrid Systems Diagnosis (SCAPHD) is developed to facilitate the derivation of GARRs. The SCAPHD extends the classical SCAP by introducing the concept of preferred causality of controlled junctions. An application to a hydraulic is used to illustrate this method

Keywords: Estimations and identification, Signal processing
Abstract: In this paper, we study modelling of rational spectra from time-domain measurements when the measurement information is not complete. We propose a three-stage estimation scheme. In the first-stage, rational spectra are identified by a modified covariance-based subspace identification algorithm in the innovation form. Accuracy of this stage is further enhanced by integrating a regularized nuclear norm optimization step with the subspace algorithm, which makes the model order selection easier. When the covariance estimates are not reliable, a modification of the optimization criterion is suggested. The second-stage of the scheme extracts a spectral factor via the recently introduced the regularized and re-weighted nuclear norm heuristic. In the third-stage, the missing values are recovered by one-step/multi-step prediction filters or fixed-interval smoothing filters. The predictors or the smoothers are built on the second-stage models in the innovation form.

Keywords: Fault detection and Diagnostics, Linear and nonlinear systems, Multivariable control
Abstract: This paper investigates the problem of fault tolerant control for discrete-time switched systems under an arbitrary switching signal. Sufficient conditions of designing an observer are obtained by using multiple Lyapunov function and {H}_{infty} technique. A Sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities (LMIs). The obtained results are applied on a numerical example showing fault detection, localization of fault and reconfiguration of the control to maintain asymptotic stability even in the presence of a permanent sensor fault.